GITNUXSOFTWARE ADVICE
AI In IndustryTop 10 Best Customer Service AI Software of 2026
Top 10 Customer Service Ai Software ranked by support automation, with Intercom, Zendesk, and Salesforce Service Cloud Einstein compared for teams.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
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Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Intercom
AI in Conversations that drafts responses from customer context and knowledge
Built for support teams using chat-first workflows needing AI-assisted resolution at scale.
Zendesk
Editor pickZendesk AI summarization for conversations inside the agent workspace
Built for customer support teams using omnichannel ticketing who want AI drafting and automation.
Salesforce Service Cloud Einstein
Editor pickEinstein Case Classification for automated case insights and predictive routing
Built for enterprises standardizing service operations on Salesforce with AI agent assistance.
Related reading
Comparison Table
This comparison table benchmarks Customer Service AI tools by integration depth, including how each platform maps customer and ticket data into its data model and schema. It also compares automation and the API surface for provisioning, extensibility, and throughput, alongside admin and governance controls like RBAC and audit log coverage. Readers can use these dimensions to evaluate support automation tradeoffs across Intercom, Zendesk, Salesforce Service Cloud Einstein, Microsoft Copilot for Service, Google Cloud Contact Center AI, and similar platforms.
Intercom
customer messagingProvides AI-assisted customer support with chatbots, agent copilots, and automated ticket handling inside a customer messaging platform.
AI in Conversations that drafts responses from customer context and knowledge
Intercom supports AI assistant experiences inside customer messaging workflows, including agent assist for drafting replies and automated responses for common questions. It connects conversations to support workflows, so knowledge and prior interaction history can shape answer relevance before or during human handoff. Teams also use ticketing-linked context to keep resolutions consistent across channels.
A key tradeoff is that answer quality depends on the quality and coverage of connected knowledge sources, because gaps can lead to generic or incorrect suggested replies. A practical fit appears for support teams that handle high-volume inbound messaging and need faster first responses while routing complex issues to agents in the same chat thread.
- +AI guidance is designed around existing customer conversation context
- +Strong automation options for triage, routing, and response support
- +Human handoff keeps agent workflows inside the same interface
- –Advanced AI tuning can require deeper admin and workflow setup
- –Complex edge cases may still depend on high-quality knowledge coverage
- –Automation outcomes can be harder to predict across diverse ticket types
Customer support operations teams
Reduce response time in chat support
Lower average handle time
Ecommerce customer support teams
Deflect order status questions
Fewer tickets for routine requests
Show 2 more scenarios
Product support enablement managers
Standardize answers across agents
More consistent customer outcomes
Knowledge-driven guidance helps agents apply consistent troubleshooting steps and phrasing during handoffs.
SaaS incident support teams
Escalate complex cases within chat
Faster escalation to specialists
AI narrows scope and routes issues to agents with relevant history for quicker investigation starts.
Best for: Support teams using chat-first workflows needing AI-assisted resolution at scale
More related reading
Zendesk
helpdesk suiteDelivers AI-powered agent assistance, ticket automation, and self-service help features for customer support workflows.
Zendesk AI summarization for conversations inside the agent workspace
Zendesk stands out with tight customer service operations built around ticketing, omnichannel inboxes, and agent workflows. Zendesk AI can draft replies, summarize conversations, and automate common support actions to reduce manual effort.
The platform also supports knowledge management, macro-based routing, and reporting for contact center performance tracking. It works best when teams need AI assistance inside a mature helpdesk and ticket lifecycle.
- +AI-assisted ticket summarization accelerates triage and reduces context switching
- +Omnichannel routing centralizes email, chat, and messaging into one workflow
- +Robust macros, SLAs, and triggers automate repetitive support processes
- –Admin setup for AI and automations can require significant configuration effort
- –AI response quality depends heavily on knowledge coverage and consistent ticket tagging
- –Advanced routing and reporting sometimes need careful workflow design
Support managers
Reduce handling time on ticket queues
Lower average handle time
Customer support agents
Handle multi-channel customer questions faster
More accurate first replies
Show 2 more scenarios
Contact center operations
Automate routing for repeat issues
Fewer misrouted tickets
Macros and AI-supported actions streamline triage for common themes in incoming requests.
Knowledge managers
Maintain help center content with AI
Higher self-service resolution
Summarization and reporting support creation and refinement of articles for recurring support topics.
Best for: Customer support teams using omnichannel ticketing who want AI drafting and automation
Salesforce Service Cloud Einstein
enterprise CRMUses AI to recommend next best actions, summarize cases, and automate responses within enterprise customer service workflows.
Einstein Case Classification for automated case insights and predictive routing
Salesforce Service Cloud Einstein stands out by embedding AI directly inside Salesforce Service Cloud case management, search, and agent workflows. It delivers automated assistance through Einstein for Service, including predictive routing, suggested replies, and knowledge recommendations that help agents resolve issues faster.
Natural-language search and Einstein search improve how support teams find relevant articles and prior cases across Salesforce data. The solution also supports AI-powered chat and workflow actions that can be tailored for service channels and customer experiences.
- +Predictive case routing improves assignment accuracy across support queues
- +Agent assist suggests next steps and recommended knowledge during live work
- +Einstein search finds relevant cases and articles from the Salesforce knowledge base
- +Supports end-to-end service workflows with AI-powered automation hooks
- –Deep setup requires strong Salesforce admin skills for reliable performance
- –AI output quality depends heavily on knowledge article coverage and structure
- –Limited visibility into model rationale compared with some specialized support AIs
Customer support agents
Draft suggested replies during case handling
Faster resolutions and fewer escalations
Service operations teams
Automate routing and assignment decisions
Improved first-contact resolution rates
Show 2 more scenarios
Knowledge managers
Recommend relevant knowledge articles
Higher knowledge reuse
Einstein surfaces articles for each inquiry using natural-language search over Salesforce knowledge and cases.
Support managers
Optimize agent workflow actions
Consistent customer experiences
AI-driven workflow steps trigger tools, updates, and chat responses aligned to service channel playbooks.
Best for: Enterprises standardizing service operations on Salesforce with AI agent assistance
More related reading
Microsoft Copilot for Service
enterprise copilotsAdds generative AI to customer service agents with case summarization, answer generation, and workflow assistance in Microsoft ecosystems.
Copilot answer grounding with service knowledge and case context for suggested replies
Microsoft Copilot for Service stands out by embedding AI assistance directly into the customer service agent workflow with Microsoft 365 and Dynamics 365. It can draft responses, summarize cases, and suggest next actions using knowledge sources connected to enterprise content.
It also supports guided experiences for faster resolution by turning ticket context into structured recommendations and follow-up questions. The tool is strongest where case management, knowledge articles, and CRM data are already standardized for service teams.
- +Summarizes cases into agent-ready overviews and timelines
- +Drafts response text aligned to ticket context and knowledge content
- +Suggests next best actions for faster resolution workflows
- +Integrates with Dynamics 365 and knowledge articles for consistent answers
- +Supports consistent agent assistance across channels and case types
- –Quality depends heavily on curated knowledge and clean case data
- –Less effective for organizations without standardized CRM and ticket structure
- –Trust controls require active governance to reduce ungrounded suggestions
- –Complex multi-product service workflows can need careful configuration
Best for: Service teams using Dynamics 365 needing AI-assisted case resolution
Google Cloud Contact Center AI
contact center AIApplies AI to contact center operations with conversational analytics and assistance features for agents and customers.
Agent Assist with real-time guidance for contact center agents
Google Cloud Contact Center AI stands out by combining contact-center specific AI with Google Cloud infrastructure, including Dialogflow and data pipelines. It supports AI agents for customer interactions, agent assist for live guidance, and analytics that connect conversation outcomes to operational metrics. Tight integration with Google Cloud services supports speech, language understanding, and workflow automation for multichannel contact center environments.
- +Strong Dialogflow integration for intent and conversation management
- +Agent assist capabilities improve handling with live guidance
- +Speech and language tooling supports automated understanding across channels
- –Implementation requires Google Cloud architecture knowledge and setup
- –Customization for complex contact flows can take more engineering effort
- –Operational tuning is needed to keep routing and AI responses accurate
Best for: Enterprises standardizing AI customer service on Google Cloud
Amazon Connect Customer Profiles and Contact Lens
contact center platformCombines AI-enhanced contact center capabilities such as customer profiles and voice analytics to improve agent performance and customer outcomes.
Contact Lens transcript search and call insights across recorded customer interactions
Amazon Connect Customer Profiles pairs with Amazon Connect for identity-linked service workflows instead of standalone chatbot-only experiences. It creates a unified customer profile from contact and CRM sources and exposes attributes for routing, personalization, and automated responses.
Contact Lens adds call analytics and search across recorded conversations to surface the reasons behind escalations and agent outcomes. Together, the stack supports better context during customer service interactions and continuous improvement driven by real call insights.
- +Customer Profiles unifies identity fields for personalization across voice and digital touchpoints
- +Contact Lens provides searchable transcripts plus call analytics for root-cause discovery
- +Integrates directly with Amazon Connect routing and contact handling workflows
- +Configurable data ingestion supports linking events to the right customer profile
- –Setup requires careful data modeling to avoid mismatched or duplicate customer identities
- –Real value depends on downstream workflow design, not just analytics outputs
- –Operational tuning for quality monitoring can take sustained effort
- –Reporting workflows across attributes and call insights can feel fragmented
Best for: Customer service teams using Amazon Connect that need unified profiles and call intelligence
More related reading
Genesys Cloud CX
enterprise contact centerUses AI-driven automation and agent assistance across omnichannel customer interactions for contact center support operations.
Genesys AI-based routing and agent assist that surfaces recommendations during live customer interactions
Genesys Cloud CX stands out with a unified contact center and AI suite built around real-time orchestration and automation. It supports customer service AI through AI-assisted routing, virtual assistant capabilities, and agent assist features that summarize interactions and recommend next actions.
The platform also includes strong workflow and integration surfaces for embedding bots and routing logic into multichannel customer journeys. Reporting and quality tools help teams track automation performance and agent outcomes across voice, chat, and digital channels.
- +Built-in AI routing and agent assist improve handling speed and consistency
- +Strong omnichannel coverage for voice, chat, and digital customer service workflows
- +Workflow automation connects bots, queues, and routing decisions using configurable logic
- +Quality and analytics support continuous improvement for automated and assisted service
- –Advanced orchestration and AI setup can require specialized admin configuration
- –Complex journeys may increase configuration effort across teams and channels
- –Some AI outputs need tuning to match domain terminology and customer intent
Best for: Contact centers needing omnichannel AI routing and agent assist in one platform
Gorgias
ecommerce supportProvides AI-assisted customer support for ecommerce teams with automated replies, macros, and workflow-driven ticket handling.
AI agents that generate replies inside the helpdesk with configurable automation
Gorgias stands out with a customer service AI workflow designed around ecommerce helpdesk operations. It combines AI-assisted agents, automation rules, and a unified inbox to handle multichannel customer messages.
Core capabilities include ticket routing, canned responses, AI-generated replies, and macros for faster resolution across support requests. It also supports analytics to track deflection and agent performance tied to conversational outcomes.
- +Unified inbox for multiple support channels with AI-assisted replies
- +Automation rules reduce manual triage and speed ticket handling
- +Macros and templates help standardize responses across common issues
- +Analytics reveal which automations and responses improve performance
- –AI responses can require frequent review for accuracy on edge cases
- –Advanced workflows take time to model and maintain in large catalogs
- –Complex routing logic can become harder to debug than simple setups
Best for: Ecommerce support teams automating ticket triage and AI-assisted responses
More related reading
Kustomer
customer service CRMOffers AI-enabled customer service and unified customer profiles to automate responses and assist support agents.
AI-assisted case automation using Kustomer’s unified customer profile
Kustomer stands out for AI-driven service automation built on a unified customer profile that connects conversations, tickets, and context. Core capabilities include automated routing, suggested replies, and deflection that can act directly inside customer service workflows.
It also supports case management and omnichannel engagement so AI outputs can be tied to resolution states. Stronger value appears when teams need consistent service experiences across messaging, email, and social-like channels with centralized history.
- +Unified customer profile keeps AI suggestions grounded in full conversation history
- +AI assists agents with replies and automations tied to case workflows
- +Omnichannel context supports consistent service across multiple customer touchpoints
- +Workflow and routing features help scale support operations with less manual work
- –Implementation effort rises when customizing AI behaviors across complex journeys
- –Admin configuration can be heavy for teams without workflow ownership
- –AI outcomes depend on data quality inside the customer profile
Best for: Customer service teams needing omnichannel AI with centralized customer context and workflows
Freshworks Freddy AI for Customer Service
customer support AIAdds AI capabilities to support ticket workflows with suggested replies, automation, and agent guidance in customer service platforms.
AI suggested replies within the ticket workspace using conversation and knowledge context
Freshworks Freddy AI for Customer Service emphasizes agent-assist workflows inside Freshworks ticketing rather than standalone chatbots. It provides AI summarization, suggested replies, and knowledge-based responses that use customer context from conversations and tickets.
It also supports intent handling to route and guide tickets toward the right resolution path. The tool is geared toward faster agent work and more consistent answers across high-volume support queues.
- +Tight integration with Freshworks tickets for context-aware suggestions
- +AI summaries reduce time spent reading long conversation threads
- +Suggested replies speed agent handling and improve tone consistency
- –Less compelling for teams without existing Freshworks service workflows
- –Knowledge-grounding quality depends heavily on curated help content
- –Automation scope feels narrower than full omnichannel AI copilots
Best for: Customer support teams already using Freshworks workflows for agent assistance
Conclusion
After evaluating 10 ai in industry, Intercom stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right Customer Service Ai Software
This guide covers customer service AI software built for real support workflows, including Intercom, Zendesk, Salesforce Service Cloud Einstein, Microsoft Copilot for Service, Google Cloud Contact Center AI, Amazon Connect Customer Profiles and Contact Lens, Genesys Cloud CX, Gorgias, Kustomer, and Freshworks Freddy AI.
The buying focus targets integration depth, data model fit, automation and API surface readiness, and admin and governance controls across these tools. It also maps each tool to support automation outcomes through support-thread drafting, case classification, and contact-center agent assist patterns.
Customer service AI that drafts, routes, and automates support work inside real service systems
Customer service AI software uses AI assistance to draft replies, summarize conversations, classify or route cases, and trigger support actions inside helpdesk, CRM, or contact-center workflows. The main job is reducing agent time in triage and response while keeping answers grounded in the connected knowledge and case context.
Tools like Intercom support AI in conversations that drafts responses from customer context and knowledge, while Zendesk AI summarizes conversations inside the agent workspace to accelerate triage. Salesforce Service Cloud Einstein embeds AI assistance directly into case workflows with predictive routing and Einstein Case Classification for automated case insights.
Evaluation criteria that predict automation control and safe answer grounding
Support AI outcomes depend on how deeply the tool connects to ticket, case, profile, and knowledge data models. Intercom and Zendesk both tie AI output to conversation or ticket context, so governance and data coverage are the difference between accurate drafting and generic responses.
Automation control also depends on the tool’s automation hooks, routing logic, and admin controls for configuration and auditing. Genesys Cloud CX and Amazon Connect use orchestration around routing and agent assist, while Salesforce Service Cloud Einstein and Microsoft Copilot for Service integrate AI into case management and CRM search.
Integration depth into ticket, case, and conversation workflows
Look for AI that operates inside the agent workspace where agents actually work. Intercom drafts responses from customer context and knowledge inside customer messaging workflows, and Zendesk AI summarizes conversations inside the agent workspace tied to the ticket lifecycle.
Knowledge grounding tied to answer generation
Strong grounding requires connected knowledge coverage that matches the AI task being executed. Microsoft Copilot for Service grounds suggested replies using service knowledge and case context, while Salesforce Service Cloud Einstein and Intercom depend on knowledge article coverage and structure to avoid generic or incorrect outputs.
Automation and routing primitives with predictable outcomes
AI assistance must connect to triage, routing, and response actions that admins can configure and monitor. Zendesk pairs Zendesk AI summarization with macros, SLAs, and triggers for repetitive support actions, while Salesforce Einstein uses predictive routing and Einstein Case Classification for automated case insights.
Automation plus real-time agent assist for live work
Agent assist reduces time spent searching and reading long threads during live handling. Google Cloud Contact Center AI and Genesys Cloud CX provide agent assist with real-time guidance and recommendations, which helps scale handling speed across voice and digital conversations.
Customer identity and context modeling across channels
Unified context improves personalization and routing when customers interact across voice and digital touchpoints. Amazon Connect Customer Profiles unifies identity-linked service workflows using contact and CRM sources, and Kustomer builds AI on a unified customer profile that connects conversations and tickets.
Admin and governance control for setup, tuning, and trust
AI configuration and trust controls require clear ownership, because output quality depends on admin setup and governance. Intercom and Zendesk can require deeper admin and workflow setup for advanced tuning, and Microsoft Copilot for Service needs active governance to reduce ungrounded suggestions.
A workflow-first decision path for choosing customer service AI
Start from the work the support team already does and choose AI that executes inside that same workflow object. Intercom fits chat-first operations where AI drafts inside the same messaging thread, while Zendesk fits omnichannel inboxes built on a ticket lifecycle.
Then confirm the data model and automation hooks that power routing and answer grounding. Tools like Amazon Connect and Genesys Cloud CX emphasize contact-center orchestration, while Salesforce Service Cloud Einstein and Microsoft Copilot for Service emphasize CRM case workflows and knowledge search.
Match the tool to the system of record for support work
If the system of record is a messaging thread, choose Intercom because AI in conversations drafts responses from customer context and knowledge while keeping human handoff inside the same interface. If the system of record is an omnichannel helpdesk with ticket states, choose Zendesk because Zendesk AI summarizes conversations inside the agent workspace and automation runs through macros, triggers, and SLAs.
Validate knowledge grounding against real coverage gaps
Check whether the connected knowledge base covers the queries the AI will handle and whether article structure supports consistent answering. Microsoft Copilot for Service and Salesforce Service Cloud Einstein both depend on curated knowledge and article structure, so missing or inconsistent articles will directly increase generic or incorrect suggested replies.
Design automation actions with routing and triage primitives
Map each automation use case to a concrete workflow action like predictive routing, classification, macros, or trigger-based execution. Salesforce Service Cloud Einstein supports predictive case routing via Einstein Case Classification, and Zendesk automates repetitive support processes using macros, SLAs, and triggers.
Plan for real-time agent assist where speed matters
For high-throughput contact center handling, prioritize agent assist that recommends next actions while the agent is live. Google Cloud Contact Center AI and Genesys Cloud CX provide agent assist and recommendations during live interactions, which reduces context switching during calls or multichannel sessions.
Confirm data model fit for identity and context continuity
If routing and personalization must survive across voice and digital touchpoints, choose Amazon Connect Customer Profiles because it builds a unified customer profile from contact and CRM sources. If the organization needs one profile that ties conversations, tickets, and resolution states, choose Kustomer for unified customer context used by AI-assisted case automation.
Set governance guardrails before expanding automation scope
Establish review loops and admin ownership for AI tuning because advanced tuning can require deeper workflow setup. Intercom and Zendesk can require significant configuration effort for AI and automation, and Microsoft Copilot for Service needs governance to reduce ungrounded suggestions before scaling beyond guided cases.
Which teams get the highest control and automation payoff
Different customer service AI tools align with different operational shapes, like chat-first support, omnichannel ticketing, CRM-first case management, or contact-center orchestration. The best fit shows up where AI execution happens inside the same workflow objects agents and admins already manage.
The following segments reflect the actual best-for positioning for each tool based on where it delivers the most predictable automation outcomes.
Chat-first support teams that need AI drafting and triage inside the conversation
Intercom fits chat-first workflows because AI in conversations drafts responses from customer context and knowledge while keeping human handoff inside the same interface. The combination of strong automation options for triage, routing, and response support suits high-volume inbound messaging.
Omnichannel helpdesk teams that run automation through tickets and macros
Zendesk fits teams that want AI inside a mature ticket lifecycle with omnichannel inbox routing. Zendesk AI summarization plus macros, SLAs, and triggers creates actionable automation while keeping routing and reporting tied to ticket operations.
Enterprises standardizing service on Salesforce for case workflow automation
Salesforce Service Cloud Einstein fits organizations that want predictive routing, suggested replies, and knowledge recommendations inside Salesforce Service Cloud case management. Einstein Case Classification supports automated case insights that align AI actions with case ownership and queue assignment.
Service teams running Microsoft 365 and Dynamics 365 that need grounded suggested replies
Microsoft Copilot for Service fits Dynamics 365 service teams because it drafts response text aligned to ticket context and knowledge articles. Guided experiences and next best action suggestions work best when case management and knowledge are standardized for service teams.
Contact centers that need omnichannel AI routing plus real-time agent assist
Genesys Cloud CX fits contact centers that need omnichannel AI routing and agent assist in one platform through workflow orchestration. Google Cloud Contact Center AI also fits Google Cloud standardization needs with Dialogflow integration and agent assist guidance for live handling.
Pitfalls that break automation quality, governance, and rollout pace
Most deployment failures come from mismatches between AI tasks and the data and workflow objects they must rely on. Several tools highlight that output quality depends heavily on knowledge coverage and consistent tagging, and that advanced tuning can require non-trivial admin configuration.
These pitfalls show up in how teams expand automation scope without governance guardrails or without verifying that identity and case data stay consistent across channels.
Launching AI reply generation without enough knowledge coverage
Intercom and Salesforce Service Cloud Einstein both depend on connected knowledge quality, so gaps lead to generic or incorrect suggested replies. Microsoft Copilot for Service and Zendesk also rely on curated knowledge and consistent ticket tagging, so coverage gaps show up as lower-quality drafts.
Over-automating triage without mapping AI outputs to monitorable routing actions
Zendesk automation outcomes can be harder to predict across diverse ticket types if triggers and macros are not modeled carefully. Salesforce Einstein predictive routing and classification still require setup and queue design, so case workflows must map to observable assignment outcomes.
Treating contact identity as optional when routing must personalize across channels
Amazon Connect Customer Profiles requires careful data modeling to avoid mismatched or duplicate identities, and poor modeling breaks personalization. Kustomer also depends on data quality inside the unified customer profile, so weak profile completeness reduces the grounding of AI-assisted case automation.
Skipping governance controls that prevent ungrounded suggestions from reaching customers
Microsoft Copilot for Service explicitly needs active governance to reduce ungrounded suggestions, so expanding scope without controls increases risk. Intercom and Zendesk can require deeper admin and workflow setup for advanced tuning, so governance must cover configuration, review, and escalation paths.
How We Selected and Ranked These Tools
We evaluated Intercom, Zendesk, Salesforce Service Cloud Einstein, Microsoft Copilot for Service, Google Cloud Contact Center AI, Amazon Connect Customer Profiles and Contact Lens, Genesys Cloud CX, Gorgias, Kustomer, and Freshworks Freddy AI for Customer Service using features, ease of use, and value criteria, with features carrying the most weight at 40 percent. Ease of use and value each account for 30 percent, and the overall rating is a weighted average of these criteria across each tool.
Intercom separated from lower-ranked tools because it delivers AI in conversations that drafts responses from customer context and knowledge, while also providing strong automation options for triage, routing, and response support. That combination lifts both features and operational usability for chat-first teams, which increased its overall fit for support automation outcomes.
Frequently Asked Questions About Customer Service Ai Software
How do Intercom, Zendesk, and Salesforce Einstein compare for support automation inside the agent workflow?
Which tool is better for automation that depends on knowledge coverage: Intercom AI in Conversations or Zendesk AI in tickets?
What integration and API surfaces matter when embedding customer service AI into existing systems?
How does identity and access management differ between tools that run AI inside customer service apps?
What data model and schema concerns affect data migration for customer profiles and case history before turning on AI?
How do admin controls and governance work when AI suggestions can trigger actions, not just drafts?
What is the typical failure mode when AI answers are wrong, and how do tools reduce it in workflow?
Which platform is most suitable for voice-centric contact centers that need real-time agent assist?
How should teams validate AI automation throughput and quality before routing large volumes of tickets?
Tools reviewed
Primary sources checked during evaluation.
aws.amazon.comReferenced in the comparison table and product reviews above.
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